Towards memory-efficient processing-in-memory architecture for convolutional neural networks

2017 ◽  
Author(s):  
Yi Wang ◽  
Mingxu Zhang ◽  
Jing Yang
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Memory Architecture ◽  
Efficient Processing ◽  
Memory Efficient

scholarly journals FruitVegCNN: Power- and Memory-Efficient Classification of Fruits & Vegetables Using CNN in Mobile MPSoC

2020 ◽  
Author(s):  
Somdip Dey ◽  
Suman Saha ◽  
Amit Singh ◽  
Klaus D. Mcdonald-Maier
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Power Efficiency ◽  
Fruits And Vegetables ◽  
State Of The Art ◽  
Agricultural Industry ◽  
Power Efficient ◽  
System On A Chip ◽  
Memory Efficient

<div><div><div><p>Fruit and vegetable classification using Convolutional Neural Networks (CNNs) has become a popular application in the agricultural industry, however, to the best of our knowledge no previously recorded study has designed and evaluated such an application on a mobile platform. In this paper, we propose a power-efficient CNN model, FruitVegCNN, to perform classification of fruits and vegetables in a mobile multi-processor system-on-a-chip (MPSoC). We also evaluated the efficacy of FruitVegCNN compared to popular state-of-the-art CNN models in real mobile plat- forms (Huawei P20 Lite and Samsung Galaxy Note 9) and experimental results show the efficacy and power efficiency of our proposed CNN architecture.</p></div></div></div>

Deep learning applied to seismic attribute computation

2019 ◽  
Vol 7 (3) ◽  
pp. SE141-SE150
Author(s):  
Donald P. Griffith ◽  
S. Ahmad Zamanian ◽  
Jeremy Vila ◽  
Antoine Vial-Aussavy ◽  
John Solum ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Seismic Attributes ◽  
Deep Convolutional Neural Networks ◽  
Seismic Attribute ◽  
Seismic Surveys ◽  
Order Of Magnitude ◽  
User Guidance ◽  
Memory Efficient

We have trained deep convolutional neural networks (DCNs) to accelerate the computation of seismic attributes by an order of magnitude. These results are enabled by overcoming the prohibitive memory requirements typical of 3D DCNs for segmentation and regression by implementing a novel, memory-efficient 3D-to-2D convolutional architecture and by including tens of thousands of synthetically generated labeled examples to enhance DCN training. Including diverse synthetic labeled seismic in training helps the network generalize enabling it to accurately predict seismic attribute values on field-acquired seismic surveys. Once trained, our DCN tool generates attributes with no input parameters and no additional user guidance. The DCN attribute computations are virtually indistinguishable from conventionally computed attributes while computing up to 100 times faster.

scholarly journals FruitVegCNN: Power- and Memory-Efficient Classification of Fruits & Vegetables Using CNN in Mobile MPSoC

2020 ◽  
Author(s):  
Somdip Dey ◽  
Suman Saha ◽  
Amit Singh ◽  
Klaus D. Mcdonald-Maier
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Power Efficiency ◽  
Fruits And Vegetables ◽  
State Of The Art ◽  
Agricultural Industry ◽  
Power Efficient ◽  
System On A Chip ◽  
Memory Efficient

<div><div><div><p>Fruit and vegetable classification using Convolutional Neural Networks (CNNs) has become a popular application in the agricultural industry, however, to the best of our knowledge no previously recorded study has designed and evaluated such an application on a mobile platform. In this paper, we propose a power-efficient CNN model, FruitVegCNN, to perform classification of fruits and vegetables in a mobile multi-processor system-on-a-chip (MPSoC). We also evaluated the efficacy of FruitVegCNN compared to popular state-of-the-art CNN models in real mobile plat- forms (Huawei P20 Lite and Samsung Galaxy Note 9) and experimental results show the efficacy and power efficiency of our proposed CNN architecture.</p></div></div></div>

A ReRAM-Based Row-Column-Oriented Memory Architecture for Convolutional Neural Networks

2019 ◽  
Vol E102.C (7) ◽  
pp. 580-584
Author(s):  
Yan CHEN ◽  
Jing ZHANG ◽  
Yuebing XU ◽  
Yingjie ZHANG ◽  
Renyuan ZHANG ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Memory Architecture
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